
'''simple docstring'''

import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
lowerCAmelCase_     :     Union[str, Any]       =	logging.get_logger(__name__)

lowerCAmelCase_     :     Optional[int]       =	{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn.grep_linear''': '''encoder.layers.*.attention.gru_rel_pos_linear''',
    '''self_attn.relative_attention_bias''': '''encoder.layers.*.attention.rel_attn_embed''',
    '''self_attn.grep_a''': '''encoder.layers.*.attention.gru_rel_pos_const''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
lowerCAmelCase_     :     List[str]       =	[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]







def __A    (       lowerCAmelCase_					,			lowerCAmelCase_					,			lowerCAmelCase_					,			lowerCAmelCase_					,			lowerCAmelCase_			):

     for attribute in key.split("""."""			):
          _UpperCAmelCase   :    str			    =       getattr(_A					,			_A			)

     if weight_type is not None:
          _UpperCAmelCase   :    Any			    =       getattr(_A					,			_A			).shape
     else:
          _UpperCAmelCase   :    List[Any]			    =       hf_pointer.shape

     assert hf_shape == value.shape, (
         f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
         f" {value.shape} for {full_name}"
     )

     if weight_type == "weight":
          _UpperCAmelCase   :    Tuple			    =       value
     elif weight_type == "weight_g":
          _UpperCAmelCase   :    Any			    =       value
     elif weight_type == "weight_v":
          _UpperCAmelCase   :    Any			    =       value
     elif weight_type == "bias":
          _UpperCAmelCase   :    Union[str, Any]			    =       value
     else:
          _UpperCAmelCase   :    Dict			    =       value

     logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."			)







def __A    (       lowerCAmelCase_					,			lowerCAmelCase_			):
     _UpperCAmelCase   :    Optional[Any]			    =       []
     _UpperCAmelCase   :    Optional[Any]			    =       fairseq_model.state_dict()

     _UpperCAmelCase   :    List[Any]			    =       hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          _UpperCAmelCase   :    Union[str, Any]			    =       False
          if "conv_layers" in name:
               load_conv_layer(
                   _A					,			_A					,			_A					,			_A					,			hf_model.config.feat_extract_norm == """group"""					,			)
               _UpperCAmelCase   :    Optional[int]			    =       True
          else:
               for key, mapped_key in MAPPING.items():
                    if key in name or key.split("""w2v_model."""			)[-1] == name.split("""."""			)[0]:
                         _UpperCAmelCase   :    int			    =       True
                         if "*" in mapped_key:
                              _UpperCAmelCase   :    Any			    =       name.split(_A			)[0].split("""."""			)[-2]
                              _UpperCAmelCase   :    int			    =       mapped_key.replace("""*"""					,			_A			)
                         if "weight_g" in name:
                              _UpperCAmelCase   :    Any			    =       """weight_g"""
                         elif "weight_v" in name:
                              _UpperCAmelCase   :    int			    =       """weight_v"""
                         elif "bias" in name and "relative_attention_bias" not in name:
                              _UpperCAmelCase   :    List[str]			    =       """bias"""
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              _UpperCAmelCase   :    Dict			    =       """weight"""
                         else:
                              _UpperCAmelCase   :    Optional[int]			    =       None

                         set_recursively(_A					,			_A					,			_A					,			_A					,			_A			)
                    continue
          if not is_used:
               unused_weights.append(_A			)

     logger.warning(f"Unused weights: {unused_weights}"			)







def __A    (       lowerCAmelCase_					,			lowerCAmelCase_					,			lowerCAmelCase_					,			lowerCAmelCase_					,			lowerCAmelCase_			):
     _UpperCAmelCase   :    List[Any]			    =       full_name.split("""conv_layers."""			)[-1]
     _UpperCAmelCase   :    Any			    =       name.split("""."""			)
     _UpperCAmelCase   :    Any			    =       int(items[0]			)
     _UpperCAmelCase   :    Tuple			    =       int(items[1]			)

     if type_id == 0:
          if "bias" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                   f"{full_name} has size {value.shape}, but"
                   f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
               )
               _UpperCAmelCase   :    List[str]			    =       value
               logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."			)
          elif "weight" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                   f"{full_name} has size {value.shape}, but"
                   f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
               )
               _UpperCAmelCase   :    Union[str, Any]			    =       value
               logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."			)
     elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
          if "bias" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                   f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
                   " found."
               )
               _UpperCAmelCase   :    Optional[Any]			    =       value
               logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."			)
          elif "weight" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                   f"{full_name} has size {value.shape}, but"
                   f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
               )
               _UpperCAmelCase   :    Union[str, Any]			    =       value
               logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."			)
     else:
          unused_weights.append(_A			)







@torch.no_grad()
def __A    (       lowerCAmelCase_					,			lowerCAmelCase_					,			lowerCAmelCase_=None			):
     _UpperCAmelCase   :    Tuple			    =       torch.load(_A			)
     _UpperCAmelCase   :    Optional[int]			    =       WavLMConfigOrig(checkpoint["""cfg"""]			)
     _UpperCAmelCase   :    int			    =       WavLMOrig(_A			)
     model.load_state_dict(checkpoint["""model"""]			)
     model.eval()

     if config_path is not None:
          _UpperCAmelCase   :    str			    =       WavLMConfig.from_pretrained(_A			)
     else:
          _UpperCAmelCase   :    List[Any]			    =       WavLMConfig()

     _UpperCAmelCase   :    str			    =       WavLMModel(_A			)

     recursively_load_weights(_A					,			_A			)

     hf_wavlm.save_pretrained(_A			)


if __name__ == "__main__":
   lowerCAmelCase_     :     Dict       =	argparse.ArgumentParser()
   parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
   parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
   parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
   lowerCAmelCase_     :     str       =	parser.parse_args()
   convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




"""simple docstring"""



import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
a       :						Any       		=		logging.get_logger(__name__)


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = RobertaPreLayerNormConfig.from_pretrained(
        _A       ,   architectures=["""RobertaPreLayerNormForMaskedLM"""]   )

    # convert state_dict
    _UpperCAmelCase        = torch.load(hf_hub_download(repo_id=_A       ,   filename="""pytorch_model.bin"""   )   )
    _UpperCAmelCase        = {}
    for tensor_key, tensor_value in original_state_dict.items():
        # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
        if tensor_key.startswith("""roberta."""   ):
            _UpperCAmelCase        = """roberta_prelayernorm.""" + tensor_key[len("""roberta."""   ) :]

        # The original implementation contains weights which are not used, remove them from the state_dict
        if tensor_key.endswith(""".self.LayerNorm.weight"""   ) or tensor_key.endswith(""".self.LayerNorm.bias"""   ):
            continue

        _UpperCAmelCase        = tensor_value

    _UpperCAmelCase        = RobertaPreLayerNormForMaskedLM.from_pretrained(
        pretrained_model_name_or_path=_A       ,   config=_A       ,   state_dict=_A   )
    model.save_pretrained(_A   )

    # convert tokenizer
    _UpperCAmelCase        = AutoTokenizer.from_pretrained(_A   )
    tokenizer.save_pretrained(_A   )


if __name__ == "__main__":
 a       :						Tuple       		=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--checkpoint-repo''',
     default=None,
     type=str,
     required=True,
     help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 a       :						Union[str, Any]       		=		parser.parse_args()
 convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)